UK Map Plots

Inputs

import pandas as pd
from pathlib import Path

import src.utils.map_utils as mu
import os
import sys

file =  os.path.join(cwd , "data", "combined_df.csv")

df = pd.read_csv(file, index_col=0)
address_col = "address_area"
df_comb = df.groupby(address_col,as_index=False).mean(numeric_only = True)
df_comb

	address_area	amount_detected	mrl	amount_pc
0	Bedfordshire	0.387000	2.373583	0.124811
1	Berkshire	0.196747	4.103357	0.742296
2	Bristol	0.268374	3.802430	0.170047
3	Buckinghamshire	0.050278	2.033333	0.098907
4	Cambridgeshire	0.512300	3.449771	0.092681
5	Central Scotland	0.064576	2.270339	0.220675
6	Cheshire	0.331614	3.441020	0.185064
7	City of London	0.488811	3.619672	0.045385
8	Cornwall	1.455588	7.805294	0.227373
9	County Durham	0.294703	4.774372	0.665944
10	Cumbria	0.206752	2.578974	0.073641
11	Derbyshire	0.296042	2.601042	0.086572
12	Devon	0.142708	3.093401	0.705448
13	Dorset	0.191635	1.853846	0.069592
14	East Riding of Yorkshire	0.294501	2.818758	0.241039
15	East Sussex	0.143893	2.445503	0.130867
16	Essex	0.468930	3.579789	0.324051
17	Glasgow	0.040593	1.450247	0.620967
18	Gloucestershire	0.115163	2.559455	0.109277
19	Greater London	0.224690	2.950398	0.168093
20	Greater Manchester	0.296786	3.689802	0.218737
21	Hampshire	0.538559	4.580920	1.157321
22	Herefordshire	1.370909	6.681515	0.099744
23	Hertfordshire	0.170821	3.124670	0.323446
24	Kent	0.344766	3.836676	0.097400
25	Lancashire	0.660758	5.865546	0.630129
26	Leicestershire	0.421726	4.812516	0.425529
27	Lincolnshire	0.720843	4.731526	0.090207
28	Lothian	0.134729	3.337831	0.079409
29	Merseyside	0.231206	3.589798	0.300602
30	Mid Scotland and Fife	0.165261	2.422155	0.355315
31	Mid Wales	0.060000	2.200000	0.026667
32	Norfolk	0.664853	4.288339	0.099100
33	North East Scotland	0.155392	2.930369	0.580549
34	North Wales	0.116242	2.562393	0.440996
35	North Yorkshire	0.275897	4.012571	0.124751
36	Northamptonshire	0.189718	2.327817	0.151279
37	Northern Ireland	0.203477	3.522473	0.320456
38	Northumberland	3.262500	5.000000	0.326250
39	Nottinghamshire	0.143738	4.401028	0.328896
40	Oxfordshire	0.136477	2.394375	0.074221
41	Shropshire	0.408955	3.994234	0.087272
42	Somerset	0.229284	3.201288	1.566792
43	South East Wales	1.259286	6.635000	0.131259
44	South Scotland	0.037000	0.332500	0.220143
45	South Wales	0.304305	3.650880	0.887286
46	South West Wales	0.247053	3.380015	0.100246
47	South Yorkshire	0.276270	4.235202	0.068786
48	Staffordshire	0.349470	4.100943	0.076136
49	Suffolk	0.335813	3.599019	0.153761
50	Surrey	0.139565	3.829293	0.494966
51	Tyne and Wear	0.303136	3.644100	0.440443
52	Warwickshire	0.432370	4.770462	0.149471
53	West Midlands	0.169469	2.723310	0.263257
54	West Scotland	0.139068	2.318882	0.164127
55	West Sussex	0.034615	1.788462	0.037361
56	West Yorkshire	0.301004	4.128642	0.858317
57	Wiltshire	0.122906	2.839687	0.075442
58	Worcestershire	0.174147	2.542257	0.163471

m = mu.plot_map(df, 
             what_to_plot='amount_pc',region_to_plot=address_col,
             json_path=os.path.join(cwd,'src//utils//map_data//combined_json.json'),
             longitude=-3.1, latitude=54.1)

m

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